Internal thread defect detection system based on multi-vision

In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

meticulous attention you have dedicated to the review of our manuscript.It is with great appreciation that we acknowledge your recognition of our research efforts..We have modified the introduction and discussion sections to more accurately elucidate the contributions of our research.We now clearly outline the theoretical and practical significance of our findings, as well as their relevance within the wider scope of research.We have enriched the content of our paper by directly contrasting the proposed method with existing approaches in Image Stitching and Defect Detection.This comparison highlights the superior accuracy, efficiency, and computational cost-effectiveness of our method compared to current technologies.The conclusion section has been revised to summarize the quantitative results that demonstrate the efficacy of our approach.
Reviewer #2: This manuscript proposes a vision-based approach that significantly enhances the efficiency of image acquisition through sophisticated hardware architecture and addresses particular challenges in industrial inspection, contributing to the field of internal thread defect detection.However, the paper is deficient in innovation, and there is a notable absence of requisite experimental results and analytical discussion.The author should provide additional experimental data to delineate the methodology's advantages and innovations in comparison to alternative techniques for internal thread defect detection.

Author response:
We greatly appreciate the time and effort you have dedicated to reviewing our manuscript, and we thank you for acknowledging the potential of our vision-based approach in the field of internal thread defect detection.We have carefully revisited the literature review to better accentuate the innovative aspects of our hardware architecture and vision-based method.Following your suggestion, we have compiled a more comprehensive set of experimental results, which not only affirm the efficiency and effectiveness of our approach but also demonstrate its advantages over existing technologies.We have expanded the discussion section to include a more in-depth analysis of the experimental outcomes.This involves a critical comparison with other techniques, providing unequivocal evidence of the strengths and potential areas for improvement of our method.We trust that these enhancements will address the issues you have raised and are hopeful that the revised manuscript will meet the esteemed standards of your publication.We are dedicated to making a substantial contribution to the field and eagerly anticipate the opportunity for our work to be featured in your journal.wehave highlighted the responses for each query in the manuscript annotated next to the manuscript.Besides, we integrated the comments of the reviewers and gave detailed answers to each comment, clearly marking the position of the modification.As follows: 1.In the abstract, the authors are encouraged to include empirical data to delineate the superiority of their approach over manual inspection or other existing methods for detecting internal thread defects, as this would lend greater persuasive power to their claims.
Author response: Thank you for your insightful suggestion regarding the enhancement of our manuscript's abstract.We agree that the inclusion of empirical data within the abstract would significantly strengthen the presentation of our research by quantitatively demonstrating the superiority of our approach.To this end, we have now incorporated key empirical findings that clearly exhibit the advantages of our method over manual inspection and other existing techniques in detecting internal thread defects.We believe that this addition will provide readers with a concise yet compelling evidence of our approach's efficacy, right at the outset of the paper.

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In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety.However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder 2. Within the introduction, several alternative methods for internal thread defect detection are mentioned, yet there is a lack of detailed discussion regarding their limitations.To better establish the proposed method's advancement, these shortcomings should be elucidated.
Author response: In response to your valuable suggestion, we have revised the introduction to include a more detailed examination of the inherent limitations of current methods used for detecting internal thread defects.This elucidation covers aspects such as accuracy, efficiency, and applicability to different materials among these alternative approaches.By contrasting these limitations with the advantages of our proposed method, we aim to highlight the contributions our research makes to the field.

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For defect detection of internal threads, high-precision laser sensors [5] can be used.Through the integration of high-precision optical detection and ranging (LiDAR) technology and cameras [6], accurate detection of structural cracks has been achieved.This method can be applied to the detection of defects in internal threads to verify their quality.However, this method does not perform well on reflective surfaces or transparent materials, as the laser might be scattered or refracted, affecting the accuracy of measurements.
Image-based systems, utilizing linear lasers and template matching techniques [7], can quickly detect thread defects.Yet, for parts with complex or non-standard thread shapes, it may be necessary to create multiple templates, increasing the system's complexity and computational demands.Utilizing novel arrayed differential flexible eddy current sensors [8], defects on the surface of iron threads can be detected, but the detection effectiveness for non-metallic materials is limited, and the performance of eddy current sensors may be influenced by the conductivity of the test materials.Through digital image processing techniques [9], OTPG can standardize and segment internal thread images, further assessing their quality.This method depends on the quality of images, and the accuracy of the processing algorithms decreases in conditions of insufficient light or poor image quality.Moreover, complex image processing algorithms require higher computational resources and processing time, affecting the real-time nature of detection.
3. The authors may add more state-of-art computer vision articles for the integrity of the manuscript ( Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny-Devernay and YOLOv6 Algorithms; Applied Sciences.3D vision technologies for a self-developed structural external crack damage recognition robot; Automation in Construction.).
high-precision inspection.In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods.Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices.To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image.The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection.This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.
: Lines 56 to 64, Lines 76 to 92 Lines 56 to 64: In recent years, research on external thread detection has advanced considerably.Detection methods can be broadly classified into two categories: contact and non-contact.Contact methods encompass techniques such as thread gauge detection (qualitative), measuring needle detection (including two-needle and three-needle methods), thread micrometer inspection (single inspection), sample inspection, three-coordinate measurement [1], and contact scanning.Although these methods tend to be more accurate, they require skilled inspectors using specialized tools for precise operation, leading to higher labor costs and lower efficiency.Additionally, they are susceptible to subjective bias and may cause wear on the workpiece during inspection.In contrast, non-contact methods utilize optical principles, including techniques like optical microscope inspection [2], laser scanning [3], and machine vision [4].These methods simplify certain manual tasks and complex calculations through high-precision inspection equipment, enabling automated control and enhanced accuracy.Machine vision-based defect inspection, leveraging images captured by specific light sources and hardware, significantly improves the quality, efficiency, and reliability of defect detection.However, due to the influence of the diffraction limit, optical microscopes have inherent limitations in resolution, and the depth of field of optical microscopes is usually shallow.For thicker samples, it is impossible to keep the entire volume in a clear focal plane simultaneously.For sensitive materials, the use of strong lasers may cause surface damage or changes to the sample, thereby affecting the accuracy of detection results.Compared with the previous two defect detection methods, the defect detection method based on machine vision can quickly process and analyze images, achieving high-speed automatic detection, significantly improving the detection speed and efficiency of the production line, and is suitable for various types and complexities of defect detection tasks.